Joint analysis of galaxy clustering and weak lensing via simulation-based inference

通过基于模拟的推理对星系团聚和弱透镜效应进行联合分析

基本信息

批准号：
ST/V004239/1
负责人：
Florent Leclercq
金额：
$ 81.33万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ST%2FV004239%2F1
关键词：
Joint analysis galaxy clustering weak

项目摘要

Physical cosmology is dominated by a successful standard model connecting high-energy physics to observational astronomy, in which the Universe evolves from the "Big Bang" to its present state. However, we currently do not know the cause of the accelerated expansion and the masses of neutrino particles. Precise measurements of the acceleration of expansion will test the validity of general relativity and shed light on the physical nature of dark energy, an unknown form of energy that affects the Universe on the largest scales. Measuring the unknown masses of neutrinos, long thought to be zero, is an important step towards elucidating their essential nature and exploring new physics beyond the standard model of particle physics. This research programme tackles these two enthralling problems.Answering such physical questions requires extracting information from large astronomical data sets with sufficient accuracy. This is a complex problem, since changes to observables when one changes the model away from the standard model are extremely subtle. Advanced techniques are required to tease out the physics. However, existing methods rely on various simplifications and assumptions that at some level do not hold. For the next generation of galaxy surveys, it will be vital to improve upon these.As a response, my techniques represent a radical rethinking of how to analyse data, incorporating of the laws of physics and the working of instruments into computer models used within data analysis. This has only been possible to do at all in the last few years thanks to novel methods that I introduced, including statistical algorithms that drastically reduce the number of model evaluations required, and computational physics techniques that make these evaluations massively parallel. These developments form the basis of the principled methodology that I will follow, known as 'simulation-based inference'.Simulation-based inference using physical computer models will uniquely enable me, for the first time, to exploit complete observed maps of the large-scale structure of the Universe and, thus, to go beyond the results obtained with standard estimators of so-called 'correlation functions'. Consequently, the key innovation that will allow me to address the science goals is a simultaneous analysis of the two main effects observable in large-scale structure surveys: galaxy clustering and weak gravitational lensing. This development is particularly timely and significant in the context of the large-scale observational project of ESA's Euclid satellite, as it will provide unique insights into both aspects of its core programme. The use of physical computer models to jointly analyse galaxy clustering and weak gravitational lensing will reduce statistical and systematic uncertainties, increase the precision of cosmological results, and improve their accuracy with respect to standard methods based on correlation functions.My proposal consists of developing, validating, and applying new physical computer data models for galaxy survey data analysis, in order to address the two scientific objectives. This research constitutes a conceptually entirely new approach to extracting physical information from large-scale astronomical data. The proposed work will culminate with the first joint clustering-lensing analyses of synthetic, then real Euclid data via simulation-based inference. These analyses will produce cosmological information of reference quality, provided by the following deliverables: joint constraints from galaxy clustering and weak gravitational lensing on the acceleration of the expansion, and on neutrino masses.The fellowship will be hosted at the Imperial Centre for Inference and Cosmology.

物理宇宙学以成功的标准模型为主导，该模型将高能物理学与观察天文学联系起来，在该模型中，宇宙从“大爆炸”演变为目前的状态。但是，我们目前不知道加速膨胀和中微子颗粒质量的原因。膨胀加速度的精确测量将测试一般相对性的有效性，并阐明了深色能量的物理性质，这是一种未知形式的能量形式，影响最大尺度上的宇宙。长期认为，测量中微子的未知质量是零，是阐明其本质本质并探索粒子物理标准模型以外的新物理学的重要一步。该研究计划解决了这两个令人着迷的问题。解决此类物理问题需要以足够的准确性从大型天文学数据集中提取信息。这是一个复杂的问题，因为当一个人更改模型时，从标准模型中更改模型非常微妙。需要先进的技术来逗弄物理学。但是，现有方法依赖于某种级别不存在的各种简化和假设。对于下一代的星系调查，对这些调查进行改进至关重要。作为一个响应，我的技术代表了如何分析数据，纳入物理定律以及将仪器的工作纳入数据分析中使用的计算机模型中的根本性重新思考。由于我引入的新方法，包括统计算法大大减少所需的模型评估数量以及使这些评估截然不同的计算物理技术，这仅在过去几年中才有可能做到这一点。这些发展构成了我将要遵循的原则方法的基础，称为“基于仿真的推理”。基于实体计算机模型的基于仿真的推理将首次使我唯一地使我能够利用宇宙大规模结构的完整观察到的完整地图，从而超出了所谓的标准估计量所能获得的所谓标准估计量。因此，将使我能够解决科学目标的关键创新是对大规模结构调查中可观察到的两个主要影响的同时分析：星系聚类和弱重力镜头。在ESA欧几里得卫星的大规模观察项目的背景下，这种发展尤其及时，重要，因为它将为其核心计划的两个方面提供独特的见解。使用物理计算机模型共同分析星系聚类和弱重力镜头将减少统计和系统的不确定性，提高宇宙学结果的准确性，并提高其基于相关功能的标准方法的准确性。我的建议包括开发，验证，验证，验证新的计算机数据模型，以对Galaxy调查数据进行新的计算机数据模型，以确定两种科学分析，以确定两种科学对象。这项研究在概念上构成了一种从大规模天文数据中提取物理信息的全新方法。所提出的工作将以合成的第一个关节聚类透镜分析，然后通过基于仿真的推断进行合成的第一个关节聚类透镜分析。这些分析将产生参考质量的宇宙学信息，由以下可交付成果提供：银河聚类和弱重力镜头的联合限制在扩张的加速度以及中微子群众方面。该研究金将在推理和宇宙学的帝国中心举办。